Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation

Iqbal, Md. Tauhid Bin; Shoyaib, Mohammad; Ryu, Byungyong; Abdullah-Al-Wadud, M.; Chae, Oksam

doi:10.1109/tifs.2017.2695456

Cited by 34 publications

(29 citation statements)

References 56 publications

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“…Deep EXpectation (DEX) formulation [1] was proposed for age estimation based on VGG-16 architecture and a classification followed by a expected value formulation, and it got good results on FG-NET, MORPH, Adience and LAP data sets. Iqbal et al [38] proposed a local face description, Directional Age-Primitive Pattern(DAPP), which inherits discernible aging cue information and achieved higher accuracy on Adience data set. Recently, Hou et al used the R-SAAFc2+IMDB-WIKI [39] method, and achieved the state-of-the-art results on Adience benchmark.…”

Section: A Age and Gender Estimationmentioning

confidence: 99%

Age Group and Gender Estimation in the Wild With Deep RoR Architecture

Zhang¹,

Gao²,

Guo³

et al. 2017

IEEE Access

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Automatically predicting age group and gender from face images acquired in unconstrained conditions is an important and challenging task in many real-world applications. Nevertheless, the conventional methods with manually-designed features on in-the-wild benchmarks are unsatisfactory because of incompetency to tackle large variations in unconstrained images. This difficulty is alleviated to some degree through Convolutional Neural Networks (CNN) for its powerful feature representation. In this paper, we propose a new CNN based method for age group and gender estimation leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group and gender classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. In order to further improve the performance and alleviate overfitting problem, RoR model is pre-trained on ImageNet firstly, and then it is fune-tuned on the IMDB-WIKI-101 data set for further learning the features of face images, finally, it is used to fine-tune on Adience data set. Our experiments illustrate the effectiveness of RoR method for age and gender estimation in the wild, where it achieves better performance than other CNN methods. Finally, the RoR-152+IMDB-WIKI-101 with two mechanisms achieves new state-of-the-art results on Adience benchmark.

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Section: A Age and Gender Estimationmentioning

confidence: 99%

Age Group and Gender Estimation in the Wild With Deep RoR Architecture

Zhang¹,

Gao²,

Guo³

et al. 2017

IEEE Access

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“…Motivated by this, we utilize the positional information of the top two edge responses ( P 1 and P 2 ) and analyze their relation to extract further detail of the local structure of the crucial texture-primitives, such as edge and corner. The structure of Kirsch masks is designed in such a way that, for the edge pattern, the second top response ( P 2 ) appears next to the top response ( P 1 ); besides, P 2 appears orthogonally to P 1 for the corner pattern, characterizing the corner-like orthogonal texture-structure [12,21]. We provide a detailed illustration of such characteristics of Kirsch masks in Figure 3.…”

Section: Ldsp Coding Schemementioning

confidence: 99%

Local directional-structural pattern for person-independent facial expression recognition

Makhmudkhujaev¹,

Iqbal²,

Ryu³

et al. 2019

Turk J Elec Eng & Comp Sci

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Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally adaptive thresholding strategy to exclude futile flat patterns. Hence, LDSP offers a stable description of facial expressions with the explicit representation of the expression-affiliated features along with the exclusion of random futile textures. We visualize the efficacy of the proposed method in three folds. First, the LDSP descriptor possesses a moderate code-length owing to the exclusion of the futile patterns, yielding less computation time than other edge descriptors. Second, for person-independent expression recognition in benchmark datasets, LDSP demonstrates higher accuracy than existing descriptors and other state-of-the-art methods. Third, LDSP shows better performance than other descriptors against noise and low resolution, exhibiting its robustness under such uneven conditions.

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“…[12], [13]. Most of existing methods have mainly focused on single modalities or single mediums including texts [14], [15], images [16]- [19], voice/speech [11], [20], and meta-data of users on Twitter [21].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Age-Group Recognition for Opinion Video Logs using Ensemble of Neural Networks

Azani¹,

Elsayed²

2019

IJACSA

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With the wide spread usage of smartphones and social media platforms, video logging is gaining an increasing popularity, especially after the advent of YouTube in 2005 with hundred millions of views per day. It has attracted interest of many people with immense emerging applications, e.g. filmmakers, journalists, product advertisers, entrepreneurs, educators and many others. Nowadays, people express and share their opinions online on various daily issues using different forms of content including texts, audios, images and videos. This study presents a multimodal approach for recognizing the speaker's age group from social media videos. Several structures of Artificial Neural Networks (ANNs) are presented and evaluated using standalone modalities. Moreover, a two-stage ensemble network is proposed to combine multiple modalities. In addition, a corpus of videos has been collected and prepared for multimodal age-group recognition with focus on Arabic language speakers. The experimental results demonstrated that combining different modalities can mitigate the limitations of unimodal recognition systems and lead to significant improvements in the results.

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Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation

Cited by 34 publications

References 56 publications

Age Group and Gender Estimation in the Wild With Deep RoR Architecture

Age Group and Gender Estimation in the Wild With Deep RoR Architecture

Local directional-structural pattern for person-independent facial expression recognition

Multimodal Age-Group Recognition for Opinion Video Logs using Ensemble of Neural Networks

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